Hybrid Whale Optimization‐Based Energy‐Efficient Lightweight Internet of Things Framework DOI

Avishek Sinha,

Samayveer Singh, Harsh Kumar Verma

et al.

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

ABSTRACT The wireless intelligent computing paradigm has significantly provided services to various sectors in today's technology‐driven landscape. Despite its popularity, faces challenges addressing time‐sensitive tasks due the physical distance between servers from users. Edge been introduced for internet of things (IoT) as an effective complement enhance capacity handling latency‐critical tasks. However, limited resources IoT and edge nodes can lead suboptimal task management. In response these challenges, we propose a lightweight approach that leverages hybrid technique combining whale optimization algorithm (WOA) with adaptive inertia weight genetic component. This method aims efficiency offloading cloud‐edge environment. Experimental results demonstrate proposed strategy not only addresses limitations traditional methods but also achieves significant improvements, 34% increase makespan minimization, 11% reduction rejection ratio, 17% decrease execution cost, 15% improvement energy utilization compared WOAs. simulation highlight effectiveness enhancing quality service (QoS) metrics latency‐sensitive applications.

Language: Английский

The application of hybrid spider monkey optimization and fuzzy self-defense algorithms for multi-objective scientific workflow scheduling in cloud computing DOI
Mustafa Ibrahim Khaleel

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101517 - 101517

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Survey on Task Scheduling in Edge-Cloud DOI
Subham Sahoo, Sambit Kumar Mishra

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(3)

Published: Feb. 24, 2025

Language: Английский

Citations

0
Towards sustainable smart cities: Workflow scheduling in cloud of health things (CoHT) using deep reinforcement learning and moth flame optimization for edge-cloud systems DOI
Mustafa Ibrahim Khaleel

Future Generation Computer Systems, Journal Year: 2025, Volume and Issue: unknown, P. 107821 - 107821

Published: March 1, 2025

Language: Английский

Citations

0
ESFMTO: A reliable task offloading strategy based on edge server failure model in IIoT DOI
Yu-Bin Yang, Yan Chen, Ningjiang Chen

et al.

Ad Hoc Networks, Journal Year: 2025, Volume and Issue: unknown, P. 103887 - 103887

Published: May 1, 2025

Language: Английский

Citations

0
Hybrid Whale Optimization‐Based Energy‐Efficient Lightweight Internet of Things Framework DOI

Avishek Sinha,

Samayveer Singh, Harsh Kumar Verma

et al.

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

ABSTRACT The wireless intelligent computing paradigm has significantly provided services to various sectors in today's technology‐driven landscape. Despite its popularity, faces challenges addressing time‐sensitive tasks due the physical distance between servers from users. Edge been introduced for internet of things (IoT) as an effective complement enhance capacity handling latency‐critical tasks. However, limited resources IoT and edge nodes can lead suboptimal task management. In response these challenges, we propose a lightweight approach that leverages hybrid technique combining whale optimization algorithm (WOA) with adaptive inertia weight genetic component. This method aims efficiency offloading cloud‐edge environment. Experimental results demonstrate proposed strategy not only addresses limitations traditional methods but also achieves significant improvements, 34% increase makespan minimization, 11% reduction rejection ratio, 17% decrease execution cost, 15% improvement energy utilization compared WOAs. simulation highlight effectiveness enhancing quality service (QoS) metrics latency‐sensitive applications.

Language: Английский

Citations

0